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. 2025 Mar 17;20(3):e0316929.
doi: 10.1371/journal.pone.0316929. eCollection 2025.

Classification of pulmonary diseases from chest radiographs using deep transfer learning

Muneeba Shamas  1 , Huma Tauseef  1 , Ashfaq Ahmad  2 , Ali Raza  2 , Yazeed Yasin Ghadi  3 , Orken Mamyrbayev  4 , Kymbat Momynzhanova  4 , Tahani Jaser Alahmadi  5
Affiliations

Classification of pulmonary diseases from chest radiographs using deep transfer learning

Muneeba Shamas et al. PLoS One. .

Abstract

Pulmonary diseases are the leading causes of disabilities and deaths worldwide. Early diagnosis of pulmonary diseases can reduce the fatality rate. Chest radiographs are commonly used to diagnose pulmonary diseases. In clinical practice, diagnosing pulmonary diseases using chest radiographs is challenging due to Overlapping and complex anatomical Structures, variability in radiographs, and their quality. The availability of a medical specialist with extensive professional experience is profoundly required. With the use of Convolutional Neural Networks in the medical field, diagnosis can be improved by automatically detecting and classifying these diseases. This paper has explored the effectiveness of Convolutional Neural Networks and transfer learning to improve the predictive outcomes of fifteen different pulmonary diseases using chest radiographs. Our proposed deep transfer learning-based computational model achieved promising results as compared to existing state-of-the-art methods. Our model reported an overall specificity of 97.92%, a sensitivity of 97.30%, a precision of 97.94%, and an Area under the Curve of 97.61%. It has been observed that the promising results of our proposed model will be valuable tool for practitioners in decision-making and efficiently diagnosing various pulmonary diseases.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Representative chest radiographs of all classes.
Fig 2
Fig 2. Schematic representation of the proposed model.
Fig 3
Fig 3. Flow of methodology.
Fig 4
Fig 4. Representative chest radiographs of all classes after preprocessing.
Fig 5
Fig 5. Confusion matrices of the diseases.
Fig 6
Fig 6. Graphs of training and validation accuracy and loss of the proposed model.
See this image and copyright information in PMC

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